Understanding the limitations of medical reasoning in large language models

Large language models demonstrate impressive performance on standardized healthcare benchmarks, yet their deployment readiness for real-world environments remains poorly understood. Current medical benchmarks present idealized scenarios that misrepresent the complexity of actual clinical data. We systematically evaluate LLM robustness by introducing clinician-validated perturbations to MedQA that mirror authentic healthcare settings: medically irrelevant information (red herrings), clinical writing styles, and standard medical abbreviations. Our comprehensive evaluation across nine models reveals substantial fragility, with diagnostic accuracy dropping up to 9.4%. Notably, semantic distractions pose the greatest threat, while some models demonstrate relative resilience to stylistic variations and medical abbreviations. Our paper addresses a gap between benchmark performance and clinical deployment readiness, while providing a systematic framework for assessing AI robustness that can be generalized to other healthcare domains.